I am going to explain market predictions for two players Messi and Courtois. I still use fifa-23 dataset, but this time I base my prediction on one more variable - 'Club Position' to get better results. Models that I use are Random Forest Regressor and Catboost Regressor.
First model that I use is RandomForestRegressor. I have already used and shortly described in homework 1.
Coefficient of determination: 0.78 Scatter plot: predictions vs truth
SHAP Decomposition for observation L. Messi
SHAP Decomposition for observation T. Courtois
DALEX Decomposition for observation L. Messi
DALEX Decomposition for observation T. Courtois
Second model is Catboost Regressor. The main idea of boosting is to sequentially combine many weak models and thus through greedy search create a strong competitive predictive model.
Coefficient of determination: 0.84 Scatter plot: predictions vs truth
SHAP Decomposition for observation L. Messi
SHAP Decomposition for observation T. Courtois
DALEX Decomposition for observation L. Messi
DALEX Decomposition for observation T. Courtois
MLP - multi-layer perceptron is a neural network that consists of at least 3 layers. One layer usually consist of a linear and an activation.
Coefficient of determination: 0.39 Scatter plot: predictions vs truth
SHAP Decomposition for observation L. Messi
SHAP Decomposition for observation T. Courtois
DALEX Decomposition for observation L. Messi
DALEX Decomposition for observation T. Courtois